Overview

Dataset statistics

Number of variables13
Number of observations5320
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory581.9 KiB
Average record size in memory112.0 B

Variable types

Numeric11
Categorical2

Alerts

fixed acidity is highly overall correlated with typeHigh correlation
volatile acidity is highly overall correlated with typeHigh correlation
chlorides is highly overall correlated with density and 1 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxide and 1 other fieldsHigh correlation
density is highly overall correlated with chlorides and 1 other fieldsHigh correlation
alcohol is highly overall correlated with densityHigh correlation
type is highly overall correlated with fixed acidity and 3 other fieldsHigh correlation
quality is highly imbalanced (73.8%)Imbalance
citric acid has 136 (2.6%) zerosZeros

Reproduction

Analysis started2023-03-08 23:37:37.605807
Analysis finished2023-03-08 23:37:50.670651
Duration13.06 seconds
Software versionydata-profiling vv4.0.0
Download configurationconfig.json

Variables

fixed acidity
Real number (ℝ)

Distinct106
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.2151786
Minimum3.8
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:50.738486image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum3.8
5-th percentile5.6
Q16.4
median7
Q37.7
95-th percentile9.8
Maximum15.9
Range12.1
Interquartile range (IQR)1.3

Descriptive statistics

Standard deviation1.3196707
Coefficient of variation (CV)0.18290201
Kurtosis4.589079
Mean7.2151786
Median Absolute Deviation (MAD)0.6
Skewness1.6504172
Sum38384.75
Variance1.7415307
MonotonicityNot monotonic
2023-03-08T17:37:50.850820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.8 279
 
5.2%
6.6 269
 
5.1%
6.4 246
 
4.6%
6.9 225
 
4.2%
7 223
 
4.2%
6.7 212
 
4.0%
7.2 201
 
3.8%
7.1 200
 
3.8%
6.5 197
 
3.7%
6.2 177
 
3.3%
Other values (96) 3091
58.1%
ValueCountFrequency (%)
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4.2 2
 
< 0.1%
4.4 3
 
0.1%
4.5 1
 
< 0.1%
4.6 2
 
< 0.1%
4.7 6
 
0.1%
4.8 9
 
0.2%
4.9 6
 
0.1%
5 26
0.5%
ValueCountFrequency (%)
15.9 1
< 0.1%
15.6 2
< 0.1%
15.5 1
< 0.1%
15 1
< 0.1%
14.3 1
< 0.1%
14.2 1
< 0.1%
14 1
< 0.1%
13.8 1
< 0.1%
13.7 1
< 0.1%
13.5 1
< 0.1%

volatile acidity
Real number (ℝ)

Distinct187
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3441297
Minimum0.08
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:50.962914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.08
5-th percentile0.16
Q10.23
median0.3
Q30.41
95-th percentile0.68
Maximum1.58
Range1.5
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.16824826
Coefficient of variation (CV)0.48890944
Kurtosis2.8631745
Mean0.3441297
Median Absolute Deviation (MAD)0.08
Skewness1.5045572
Sum1830.77
Variance0.028307477
MonotonicityNot monotonic
2023-03-08T17:37:51.070857image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.28 232
 
4.4%
0.24 219
 
4.1%
0.26 219
 
4.1%
0.27 188
 
3.5%
0.25 186
 
3.5%
0.22 183
 
3.4%
0.2 178
 
3.3%
0.23 178
 
3.3%
0.3 168
 
3.2%
0.32 164
 
3.1%
Other values (177) 3405
64.0%
ValueCountFrequency (%)
0.08 2
 
< 0.1%
0.085 1
 
< 0.1%
0.09 1
 
< 0.1%
0.1 6
 
0.1%
0.105 4
 
0.1%
0.11 9
 
0.2%
0.115 3
 
0.1%
0.12 29
0.5%
0.125 3
 
0.1%
0.13 36
0.7%
ValueCountFrequency (%)
1.58 1
< 0.1%
1.33 2
< 0.1%
1.24 1
< 0.1%
1.185 1
< 0.1%
1.18 1
< 0.1%
1.13 1
< 0.1%
1.115 1
< 0.1%
1.1 1
< 0.1%
1.09 1
< 0.1%
1.07 1
< 0.1%

citric acid
Real number (ℝ)

Distinct89
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.31849436
Minimum0
Maximum1.66
Zeros136
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:51.189869image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.24
median0.31
Q30.4
95-th percentile0.56
Maximum1.66
Range1.66
Interquartile range (IQR)0.16

Descriptive statistics

Standard deviation0.14715733
Coefficient of variation (CV)0.46204063
Kurtosis2.5824713
Mean0.31849436
Median Absolute Deviation (MAD)0.07
Skewness0.48430903
Sum1694.39
Variance0.021655281
MonotonicityNot monotonic
2023-03-08T17:37:51.292475image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3 264
 
5.0%
0.32 240
 
4.5%
0.28 236
 
4.4%
0.49 232
 
4.4%
0.26 203
 
3.8%
0.34 203
 
3.8%
0.29 198
 
3.7%
0.31 188
 
3.5%
0.24 184
 
3.5%
0.27 179
 
3.4%
Other values (79) 3193
60.0%
ValueCountFrequency (%)
0 136
2.6%
0.01 31
 
0.6%
0.02 44
 
0.8%
0.03 26
 
0.5%
0.04 34
 
0.6%
0.05 23
 
0.4%
0.06 25
 
0.5%
0.07 28
 
0.5%
0.08 36
 
0.7%
0.09 36
 
0.7%
ValueCountFrequency (%)
1.66 1
 
< 0.1%
1.23 1
 
< 0.1%
1 6
0.1%
0.99 1
 
< 0.1%
0.91 1
 
< 0.1%
0.88 1
 
< 0.1%
0.86 1
 
< 0.1%
0.82 2
 
< 0.1%
0.81 2
 
< 0.1%
0.8 1
 
< 0.1%

residual sugar
Real number (ℝ)

Distinct316
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0484774
Minimum0.6
Maximum65.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:51.405497image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.1
Q11.8
median2.7
Q37.5
95-th percentile14.4
Maximum65.8
Range65.2
Interquartile range (IQR)5.7

Descriptive statistics

Standard deviation4.5001801
Coefficient of variation (CV)0.89139353
Kurtosis7.0255945
Mean5.0484774
Median Absolute Deviation (MAD)1.5
Skewness1.7065503
Sum26857.9
Variance20.251621
MonotonicityNot monotonic
2023-03-08T17:37:51.636496image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.6 200
 
3.8%
2 200
 
3.8%
1.4 194
 
3.6%
1.8 193
 
3.6%
1.2 172
 
3.2%
2.2 158
 
3.0%
1.5 150
 
2.8%
1.7 149
 
2.8%
1.9 149
 
2.8%
2.1 148
 
2.8%
Other values (306) 3607
67.8%
ValueCountFrequency (%)
0.6 1
 
< 0.1%
0.7 7
 
0.1%
0.8 25
 
0.5%
0.9 36
 
0.7%
0.95 3
 
0.1%
1 77
1.4%
1.05 1
 
< 0.1%
1.1 126
2.4%
1.15 3
 
0.1%
1.2 172
3.2%
ValueCountFrequency (%)
65.8 1
< 0.1%
31.6 1
< 0.1%
26.05 1
< 0.1%
23.5 1
< 0.1%
22.6 1
< 0.1%
22 1
< 0.1%
20.8 2
< 0.1%
20.7 1
< 0.1%
20.4 1
< 0.1%
20.3 1
< 0.1%

chlorides
Real number (ℝ)

Distinct214
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.05668985
Minimum0.009
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:51.747162image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.009
5-th percentile0.028
Q10.038
median0.047
Q30.066
95-th percentile0.104
Maximum0.611
Range0.602
Interquartile range (IQR)0.028

Descriptive statistics

Standard deviation0.036863315
Coefficient of variation (CV)0.65026305
Kurtosis48.260708
Mean0.05668985
Median Absolute Deviation (MAD)0.011
Skewness5.338237
Sum301.59
Variance0.001358904
MonotonicityNot monotonic
2023-03-08T17:37:51.850187image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.036 165
 
3.1%
0.044 160
 
3.0%
0.042 158
 
3.0%
0.046 156
 
2.9%
0.04 152
 
2.9%
0.047 148
 
2.8%
0.048 143
 
2.7%
0.038 142
 
2.7%
0.05 141
 
2.7%
0.034 138
 
2.6%
Other values (204) 3817
71.7%
ValueCountFrequency (%)
0.009 1
 
< 0.1%
0.012 2
 
< 0.1%
0.013 1
 
< 0.1%
0.014 4
 
0.1%
0.015 3
 
0.1%
0.016 5
 
0.1%
0.017 5
 
0.1%
0.018 8
0.2%
0.019 7
0.1%
0.02 13
0.2%
ValueCountFrequency (%)
0.611 1
< 0.1%
0.61 1
< 0.1%
0.467 1
< 0.1%
0.464 1
< 0.1%
0.422 1
< 0.1%
0.415 2
< 0.1%
0.414 2
< 0.1%
0.413 1
< 0.1%
0.403 1
< 0.1%
0.401 1
< 0.1%

free sulfur dioxide
Real number (ℝ)

Distinct135
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.036654
Minimum1
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:51.963188image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q116
median28
Q341
95-th percentile61
Maximum289
Range288
Interquartile range (IQR)25

Descriptive statistics

Standard deviation17.805045
Coefficient of variation (CV)0.59277723
Kurtosis9.5207058
Mean30.036654
Median Absolute Deviation (MAD)12
Skewness1.3627195
Sum159795
Variance317.01962
MonotonicityNot monotonic
2023-03-08T17:37:52.067348image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 150
 
2.8%
29 144
 
2.7%
26 134
 
2.5%
15 132
 
2.5%
24 128
 
2.4%
34 124
 
2.3%
17 124
 
2.3%
31 124
 
2.3%
23 121
 
2.3%
28 115
 
2.2%
Other values (125) 4024
75.6%
ValueCountFrequency (%)
1 2
 
< 0.1%
2 2
 
< 0.1%
3 50
 
0.9%
4 43
 
0.8%
5 111
2.1%
5.5 1
 
< 0.1%
6 150
2.8%
7 82
1.5%
8 76
1.4%
9 81
1.5%
ValueCountFrequency (%)
289 1
< 0.1%
146.5 1
< 0.1%
138.5 1
< 0.1%
131 1
< 0.1%
128 1
< 0.1%
124 1
< 0.1%
122.5 1
< 0.1%
118.5 1
< 0.1%
112 1
< 0.1%
110 1
< 0.1%

total sulfur dioxide
Real number (ℝ)

Distinct276
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.10902
Minimum6
Maximum440
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:52.184273image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile19
Q174
median116
Q3153.25
95-th percentile206
Maximum440
Range434
Interquartile range (IQR)79.25

Descriptive statistics

Standard deviation56.774223
Coefficient of variation (CV)0.49754368
Kurtosis-0.2999971
Mean114.10902
Median Absolute Deviation (MAD)39
Skewness0.063614434
Sum607060
Variance3223.3124
MonotonicityNot monotonic
2023-03-08T17:37:52.288753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111 54
 
1.0%
113 50
 
0.9%
114 49
 
0.9%
98 48
 
0.9%
122 48
 
0.9%
128 46
 
0.9%
117 44
 
0.8%
150 43
 
0.8%
101 43
 
0.8%
110 43
 
0.8%
Other values (266) 4852
91.2%
ValueCountFrequency (%)
6 2
 
< 0.1%
7 4
 
0.1%
8 11
 
0.2%
9 14
0.3%
10 24
0.5%
11 22
0.4%
12 26
0.5%
13 25
0.5%
14 30
0.6%
15 28
0.5%
ValueCountFrequency (%)
440 1
< 0.1%
366.5 1
< 0.1%
344 1
< 0.1%
313 1
< 0.1%
307.5 1
< 0.1%
303 1
< 0.1%
294 1
< 0.1%
289 1
< 0.1%
282 1
< 0.1%
278 1
< 0.1%

density
Real number (ℝ)

Distinct998
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9945353
Minimum0.98711
Maximum1.03898
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:52.395753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.98711
5-th percentile0.9898495
Q10.9922
median0.99465
Q30.99677
95-th percentile0.999161
Maximum1.03898
Range0.05187
Interquartile range (IQR)0.00457

Descriptive statistics

Standard deviation0.0029655051
Coefficient of variation (CV)0.0029817997
Kurtosis8.7114978
Mean0.9945353
Median Absolute Deviation (MAD)0.00225
Skewness0.66632582
Sum5290.9278
Variance8.7942203 × 10-6
MonotonicityNot monotonic
2023-03-08T17:37:52.510728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.992 60
 
1.1%
0.9972 59
 
1.1%
0.9928 53
 
1.0%
0.998 53
 
1.0%
0.9976 52
 
1.0%
0.9968 51
 
1.0%
0.9934 50
 
0.9%
0.9932 50
 
0.9%
0.9962 49
 
0.9%
0.9966 48
 
0.9%
Other values (988) 4795
90.1%
ValueCountFrequency (%)
0.98711 1
< 0.1%
0.98713 1
< 0.1%
0.98722 1
< 0.1%
0.9874 1
< 0.1%
0.98742 2
< 0.1%
0.98746 2
< 0.1%
0.98758 1
< 0.1%
0.98774 1
< 0.1%
0.98779 1
< 0.1%
0.98794 1
< 0.1%
ValueCountFrequency (%)
1.03898 1
< 0.1%
1.0103 1
< 0.1%
1.00369 1
< 0.1%
1.0032 1
< 0.1%
1.00315 2
< 0.1%
1.00295 1
< 0.1%
1.00289 1
< 0.1%
1.0026 2
< 0.1%
1.00242 1
< 0.1%
1.00241 1
< 0.1%

pH
Real number (ℝ)

Distinct108
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2246635
Minimum2.72
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:52.619751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum2.72
5-th percentile2.98
Q13.11
median3.21
Q33.33
95-th percentile3.5
Maximum4.01
Range1.29
Interquartile range (IQR)0.22

Descriptive statistics

Standard deviation0.1603792
Coefficient of variation (CV)0.049735174
Kurtosis0.43181116
Mean3.2246635
Median Absolute Deviation (MAD)0.11
Skewness0.38996921
Sum17155.21
Variance0.025721489
MonotonicityNot monotonic
2023-03-08T17:37:52.731728image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.16 156
 
2.9%
3.22 154
 
2.9%
3.14 146
 
2.7%
3.15 144
 
2.7%
3.2 142
 
2.7%
3.24 141
 
2.7%
3.18 139
 
2.6%
3.19 136
 
2.6%
3.12 130
 
2.4%
3.17 127
 
2.4%
Other values (98) 3905
73.4%
ValueCountFrequency (%)
2.72 1
 
< 0.1%
2.74 2
 
< 0.1%
2.77 1
 
< 0.1%
2.79 2
 
< 0.1%
2.8 3
 
0.1%
2.82 1
 
< 0.1%
2.83 3
 
0.1%
2.84 1
 
< 0.1%
2.85 6
0.1%
2.86 8
0.2%
ValueCountFrequency (%)
4.01 2
< 0.1%
3.9 2
< 0.1%
3.85 1
< 0.1%
3.82 1
< 0.1%
3.81 1
< 0.1%
3.8 2
< 0.1%
3.79 1
< 0.1%
3.78 2
< 0.1%
3.77 2
< 0.1%
3.76 2
< 0.1%

sulphates
Real number (ℝ)

Distinct111
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.53335714
Minimum0.22
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:52.842396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0.22
5-th percentile0.35
Q10.43
median0.51
Q30.6
95-th percentile0.7905
Maximum2
Range1.78
Interquartile range (IQR)0.17

Descriptive statistics

Standard deviation0.14974293
Coefficient of variation (CV)0.28075546
Kurtosis8.6129165
Mean0.53335714
Median Absolute Deviation (MAD)0.08
Skewness1.8094538
Sum2837.46
Variance0.022422944
MonotonicityNot monotonic
2023-03-08T17:37:52.954324image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.5 212
 
4.0%
0.46 196
 
3.7%
0.54 194
 
3.6%
0.44 183
 
3.4%
0.48 170
 
3.2%
0.38 165
 
3.1%
0.52 162
 
3.0%
0.47 161
 
3.0%
0.49 159
 
3.0%
0.45 157
 
3.0%
Other values (101) 3561
66.9%
ValueCountFrequency (%)
0.22 1
 
< 0.1%
0.23 1
 
< 0.1%
0.25 4
 
0.1%
0.26 3
 
0.1%
0.27 10
 
0.2%
0.28 12
 
0.2%
0.29 12
 
0.2%
0.3 24
0.5%
0.31 31
0.6%
0.32 44
0.8%
ValueCountFrequency (%)
2 1
 
< 0.1%
1.98 1
 
< 0.1%
1.95 1
 
< 0.1%
1.62 1
 
< 0.1%
1.61 1
 
< 0.1%
1.59 1
 
< 0.1%
1.56 1
 
< 0.1%
1.36 3
0.1%
1.34 1
 
< 0.1%
1.33 1
 
< 0.1%

alcohol
Real number (ℝ)

Distinct111
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.549241
Minimum8
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size83.1 KiB
2023-03-08T17:37:53.070299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile9
Q19.5
median10.4
Q311.4
95-th percentile12.7
Maximum14.9
Range6.9
Interquartile range (IQR)1.9

Descriptive statistics

Standard deviation1.1859329
Coefficient of variation (CV)0.11241879
Kurtosis-0.53816926
Mean10.549241
Median Absolute Deviation (MAD)0.9
Skewness0.54569598
Sum56121.963
Variance1.4064369
MonotonicityNot monotonic
2023-03-08T17:37:53.178308image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 288
 
5.4%
9.4 260
 
4.9%
9.2 205
 
3.9%
10 204
 
3.8%
10.5 193
 
3.6%
11 176
 
3.3%
9.8 173
 
3.3%
9.3 169
 
3.2%
10.4 166
 
3.1%
10.2 158
 
3.0%
Other values (101) 3328
62.6%
ValueCountFrequency (%)
8 2
 
< 0.1%
8.4 4
 
0.1%
8.5 10
 
0.2%
8.6 16
 
0.3%
8.7 48
 
0.9%
8.8 67
1.3%
8.9 58
1.1%
9 137
2.6%
9.05 1
 
< 0.1%
9.1 127
2.4%
ValueCountFrequency (%)
14.9 1
 
< 0.1%
14.2 1
 
< 0.1%
14.05 1
 
< 0.1%
14 11
0.2%
13.9 3
 
0.1%
13.8 2
 
< 0.1%
13.7 5
0.1%
13.6 11
0.2%
13.56666667 1
 
< 0.1%
13.55 1
 
< 0.1%

quality
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size342.9 KiB
0
5084 
1
 
236

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5320
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5084
95.6%
1 236
 
4.4%

Length

2023-03-08T17:37:53.277299image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-08T17:37:53.367312image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 5084
95.6%
1 236
 
4.4%

Most occurring characters

ValueCountFrequency (%)
0 5084
95.6%
1 236
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5320
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5084
95.6%
1 236
 
4.4%

Most occurring scripts

ValueCountFrequency (%)
Common 5320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5084
95.6%
1 236
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5084
95.6%
1 236
 
4.4%

type
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size342.9 KiB
0
3961 
1
1359 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters5320
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 3961
74.5%
1 1359
 
25.5%

Length

2023-03-08T17:37:53.434282image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-03-08T17:37:53.645294image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
0 3961
74.5%
1 1359
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0 3961
74.5%
1 1359
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5320
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3961
74.5%
1 1359
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common 5320
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3961
74.5%
1 1359
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5320
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3961
74.5%
1 1359
 
25.5%

Interactions

2023-03-08T17:37:49.291914image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:38.509341image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.553913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.622773image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.756804image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.800698image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.841720image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.009358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.029999image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.067960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.111020image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.386444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:38.605366image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.650699image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.718917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.852146image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.895722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.934642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.098358image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.130984image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.161936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.207279image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.487444image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:38.703353image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.748701image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.815917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.952262image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.995820image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.035642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.195885image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.228009image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.260936image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.309116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.584564image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:38.798317image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.845886image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.909905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.049004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.092036image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.129642image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.286625image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.323011image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.355947image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.405091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.679565image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:38.894288image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.944913image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.003917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.139992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.187037image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.224617image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.378012image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.415318image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.448144image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.500116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.776001image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:38.991316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.041499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.197917image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.234992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.279962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.318935image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.478979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.513078image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.542140image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.597116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.870979image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.086320image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.138499image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.287918image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.326992image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.371962image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.412349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.570997image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.606066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.637629image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.814091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.960410image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.173316image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.229887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.378197image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.413754image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.459961image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.501349image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.653007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.692948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.725653image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.904091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:50.052256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.265305image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.323887image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.469779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.503753image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.550949image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.595500image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.745004image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.780937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.818373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.996091image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:50.151248image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.360841image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.421137image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.563791image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.598752image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.647937image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.689226image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.837007image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.874960image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:47.909373image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.094116image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:50.251087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:39.459851image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:40.524185image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:41.660779image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:42.703722image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:43.743725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:44.909256image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:45.934014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:46.969948image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:48.010351image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-03-08T17:37:49.191926image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-03-08T17:37:53.718293image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype
fixed acidity1.0000.2070.279-0.0250.359-0.267-0.2470.450-0.2620.230-0.1140.0000.502
volatile acidity0.2071.000-0.304-0.0210.432-0.370-0.3410.3100.1790.264-0.0620.1920.657
citric acid0.279-0.3041.0000.073-0.0630.1180.1560.063-0.3050.0320.0240.1040.423
residual sugar-0.025-0.0210.0731.000-0.0330.3630.4290.493-0.194-0.118-0.2660.0260.328
chlorides0.3590.432-0.063-0.0331.000-0.261-0.2790.6090.1600.376-0.4170.0480.752
free sulfur dioxide-0.267-0.3700.1180.363-0.2611.0000.742-0.025-0.162-0.236-0.1660.1680.412
total sulfur dioxide-0.247-0.3410.1560.429-0.2790.7421.0000.022-0.229-0.264-0.2810.1180.794
density0.4500.3100.0630.4930.609-0.0250.0221.0000.0400.302-0.6830.0380.354
pH-0.2620.179-0.305-0.1940.160-0.162-0.2290.0401.0000.2350.1100.0720.315
sulphates0.2300.2640.032-0.1180.376-0.236-0.2640.3020.2351.000-0.0170.0520.473
alcohol-0.114-0.0620.024-0.266-0.417-0.166-0.281-0.6830.110-0.0171.0000.0790.133
quality0.0000.1920.1040.0260.0480.1680.1180.0380.0720.0520.0791.0000.000
type0.5020.6570.4230.3280.7520.4120.7940.3540.3150.4730.1330.0001.000

Missing values

2023-03-08T17:37:50.392075image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-08T17:37:50.577627image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype
07.40.700.001.90.07611.034.00.99783.510.569.401
17.80.880.002.60.09825.067.00.99683.200.689.801
27.80.760.042.30.09215.054.00.99703.260.659.801
311.20.280.561.90.07517.060.00.99803.160.589.801
57.40.660.001.80.07513.040.00.99783.510.569.401
67.90.600.061.60.06915.059.00.99643.300.469.401
77.30.650.001.20.06515.021.00.99463.390.4710.001
87.80.580.022.00.0739.018.00.99683.360.579.501
97.50.500.366.10.07117.0102.00.99783.350.8010.501
106.70.580.081.80.09715.065.00.99593.280.549.201
fixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholqualitytype
64876.80.2200.361.200.05238.0127.00.993303.040.549.200
64884.90.2350.2711.750.03034.0118.00.995403.070.509.400
64896.10.3400.292.200.03625.0100.00.989383.060.4411.800
64905.70.2100.320.900.03838.0121.00.990743.240.4610.600
64916.50.2300.381.300.03229.0112.00.992983.290.549.700
64926.20.2100.291.600.03924.092.00.991143.270.5011.200
64936.60.3200.368.000.04757.0168.00.994903.150.469.600
64946.50.2400.191.200.04130.0111.00.992542.990.469.400
64955.50.2900.301.100.02220.0110.00.988693.340.3812.800
64966.00.2100.380.800.02022.098.00.989413.260.3211.800